In intelligent transport systems, short term traffic forecasting is one of the most important
problems, reflecting the network state in the near future and feeding information
to other application modules. Even though there have been quite a lot of works in this
area, most of them are univariate models which may not be able to exploit the spatial
relationship of traffic variables. So, this thesis explores the domain of two renowned
modelling classes, the auto regressive moving average model and the dynamic model,
taking into account the spatial dependency.
The sparse-form vector autoregressive moving average model is applied to the short
term traffic forecasting problem with different preprocessing methods. Network information
is used to constrain the matrix parameters of the model, reducing the number
of parameters. For the estimation problem, an improved MCMC method is proposed
to tackle the variable correlation problem, using the marginalisation and the correlation
direction information. Multi-step-ahead prediction results of different models are
compared with two Dublin traffic datasets.
A second model, consisting of four sub-models, targets the multi-step-ahead flow
prediction for traffic data with incidents, where the data pattern may shift unexpectedly.
The model is designed to satisfy the scalability property so that the inference
of each component can be done conditionally independently. Furthermore, each submodel
supports sequential inference, which is essential for real-time applications. The
first two sub-models are analysed with a VISSIM dataset and the discussion of the last
two is given at the end.
A sequential approximation method is developed for both the state vector and
the parameters of the dynamic model that is part of the second model. To avoid the
degeneracy problem of the sampling-based particle filter, the method uses a continuous
functional approximation which is a modified implementation of the iterated Laplace approximation (Bornkamp, 2011a). Both the modified iterated Laplace approximation
and the sequential approximation method are illustrated and analysed with several
examples.

dc.format

1 volume

dc.language.iso

en

dc.publisher

Trinity College (Dublin, Ireland). School of Computer Science & Statistics

dc.relation.isversionof

http://stella.catalogue.tcd.ie/iii/encore/record/C__Rb15651897

dc.subject

Statistics, Ph.D.

dc.subject

Ph.D. Trinity College Dublin

dc.title

Bayesian inference for short term traffic forecasting

dc.type

thesis

dc.type.supercollection

refereed_publications

dc.type.supercollection

thesis_dissertations

dc.type.qualificationlevel

Doctoral

dc.type.qualificationname

Doctor of Philosophy (Ph.D.)

dc.rights.ecaccessrights

openAccess

dc.format.extentpagination

pp 180

dc.description.note

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